Computed tomography (“CT”) scanning enables a doctor to obtain detailed images of a patient's internal organs and tissues. CT scanning is used in a variety of medical fields, such as in radiology, cardiology and oncology, to diagnosis conditions and diseases, as well as to plan radiation treatments, for example. CT scanning has also been used in other fields to identify defects in machinery, to perform baggage inspections at airports, and to analyze the internal anatomy of preserved Egyptian mummies, for example.
To obtain a CT image, a portion of a patient or other such target is irradiated by X-ray radiation at a sufficient number of angles to enable CT image reconstruction, as is known in the art. One or more detectors are positioned or are positionable at the plurality of angles, to detect radiation transmitted through the target. The detectors convert the detected X-ray beam into electrical signals (analog signals) that are subsequently converted into digital data for input into a computer. The computer receives the digital data and processes it to reconstruct CT images for analysis.
In accordance with one common type of CT acquisition geometry, referred to as “third generation” CT, an X-ray source, such as an X-ray tube or linear accelerator, and a detector facing the source, are rotated together around a patient or other such target. FIG. 1 is a schematic representation of a front view of a third generation CT system 100 showing a source 102, a detector 104, and a patient 106 lying on a support 108, such as a bench or platform, for example. In this example, the source 102 and detector 104 are simultaneously moved around the patient 106, here in the clockwise direction, as shown by the arrows A. The source 102 and detector 104 may be supported and moved by a rotatable, circular gantry (not shown), as is known in the art. The source 102 and the detector 104 may be supported by a rotatable C-arm, as well, as shown in U.S. Patent Publication No. 2004/0068169 (“the '169 Publication”), which was filed on Oct. 5, 2002 bearing U.S. application Ser. No. 10/264,630, was published on Apr. 8, 2004, was filed on Oct. 5, 2002, and is incorporated by reference herein. The radiation emitted by the source 102 may be collimated into a fan beam or a cone beam. If a fan beam is used, the detectors 104 may comprise one-dimensional detector arrays. If a cone beam is used, the detectors 104 may comprise two dimensional detector arrays. Fan beam and cone beam reconstruction algorithms are known in the art. Ring artifacts may also appear in images generated by CT systems in which the target is rotated. In such systems, the target may be moved vertically or the source and the detector may be moved vertically. Such systems may be used to examine objects, such as cargo containers, for contraband, for example, as described in U.S. patent application Ser. No. 10/310,060, which was filed on Dec. 4, 2002, was published on Jun. 10, 2004 bearing Publication No. 2004/0109532, is assigned to the assignee of the present invention, and is incorporated by reference herein. Such systems may also be used to examine manufactured products for defects, for example.
One problem with third generation CT scanning is the occasional appearance of circular or elliptical ring artifacts in the output image. An example of a CT image with circular, ring artifacts is shown in FIG. 2. Ring artifacts may be caused by one or more faulty detectors that produce varying signal outputs. More specifically, during the rotation of the X-ray source and detector, the rays measured by a given detector are tangent to a circle. If a detector or detector element has a slight offset or gain instability, a circular artifact can appear in the output image due to rotation of that detector around the patient. In addition, mechanical instabilities of the rotatable gantry can produce elliptical rather than circular artifacts. Physical characteristics of the imaging apparatus and/or detector can also cause variable intensity and/or partial ring artifacts (semi-circular, for example). Artifacts can degrade image quality and affect the perceptibility of detail, which can cause serious problems for doctors, for example, who need to provide diagnosis and/or identify a target for treatment based on the output image.
One way to correct for ring artifacts in third generation CT scanners is to locate and recalibrate or replace the faulty detectors. Alternatively, algorithms, such as balancing algorithms, have been used to mitigate ring artifacts. Computed Tomography, Euclid Seeram, W.B. Sanders Co., 2nd Edition (2001), pp. 194-195. Examples of algorithms used to mitigate ring artifacts are described in U.S. Pat. No. 4,670,840, U.S. Pat. No. 6,115,445, U.S. Pat. No. 5,533,081, and U.S. Pat. No. 5,745,542.
Software algorithms that correct for ring artifacts are generally complex and must be performed with large amounts of data. This can delay reconstruction/generation of corrected images. During radiotherapy, for example, it is often necessary to obtain images as soon as possible.
An example of a correction algorithm is also described, in Sijbers, J., Postnov, A., “Reduction of ring artifacts in high resolution micro-CT reconstructions,” Phys. Med. Biol. 49(14); N247-53, Jul. 21, 2004, (“Sijbers”). Sijbers first transforms an input image in Cartesian coordinates into polar coordinates. Using a sliding window, a set of homogenous rows are identified in the polar image and an artifact template is generated based on the rows. The artifact template is subtracted from the polar image, and the resulting image is transformed back into Cartesian coordinates. It has been found that the quality of such a constant intensity ring correction is not always sufficient and the execution time may be prohibitively long.
Another example of a correction algorithm is described by M. Zellerhoff et al., in “Low Contrast 3D-reconstruction from C-arm data,” Medical Imaging 2005: Physics of Medical Imaging, Proceedings of SPIE Vol. 5745 (SPFE, Bellingham, Wash., 2005). First, a “reduced image” of a reconstructed image, containing only pixels associated with soft tissue, is generated. Circular structures in the image are removed by applying a median filter in the radial direction. The difference between this filtered image and the “reduced image” is generated and used as an initial ring image. A two-step smoothing in the circular direction is then performed to eliminate noise and non-circular structures. The resulting final ring image contains only the ring artifacts. The final ring image is subtracted from the original reconstructed image to obtain a corrected image. Zellerhoff states that: “for a better performance the radial and circular filtering steps are implemented using a Cartesian grid. In this case, no coordinate transformation before or after the correction is necessary.” (Id. at p. 652).